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Banking crises and recessions: What
can leading indicators tell us?
Dr. Martin Weale
Outline
1. Introduction
2. Unconditional analysis of relationship
between banking crises and recessions
3. Indicator models of banking crises and
recessions
4. Predictive power of models
5. Conclusion
Outline
2
Jointly estimating banking crises and
recessions
• We use a bivariate probit model, We set yit=1 if there is a
banking crisis and yit=0 otherwise; zit=1 if output falls in
country i in year t and zit=0 if it does not.
• The general specification of our bivariate model:
, yit=1 if y*it>0, 0 otherwise
, zit=1 if z*it>0, 0 otherwise
• where
• We begin with a simple model with no exogenous
variables so as to investigate the pattern of causality
between the two types of events
3. Indicator models of banking crises and recessions
3
A Broader Model with
Exogenous Variables
• We now introduce the explanatory
variables discussed earlier.
• The aim is first to see how far they help us
predict crises and recessions and
secondly how they affect our conclusions
about the interdependence between the
two events.
5/23/201713
October
(c) PIIE,
2009 2009
5
Equation 3: Bivariate probit model of
banking crises and recessions
Coefficient
Standard error
z
P>|z|
Banking crises
Change in liquidityt-1
-12.80
7.32
-1.75
0.081
Leveraget-1
-0.10
0.06
-1.73
0.084
Current account as % GDPt-2
-0.23
0.07
-3.40
0.001
Constant
-1.48
0.32
-4.56
0.000
Two-year change in PCIt-1
-0.28
0.08
-3.36
0.001
Two-year change in liquidityt-1
-7.19
4.02
-1.79
0.074
Real house price inflationt-1
-0.15
0.03
-4.57
0.000
Real house price inflationt-2
0.08
0.03
2.9
0.004
Constant
-2.10
0.22
-9.55
0.000
ρ
-0.15
0.36
Recessions
Number of observations:
3. Indicator models of banking crises and recessions
322 Log likelihood:
1.000
-85.87
6
Jointly estimating banking crises and
recessions
• We find that banking sector capital and liquidity ratios
and the current account deficit are useful predictors of
banking crises, but leading indicators of GDP growth do
not appear to be significant.
• Sharp falls in OECD leading indicators of GDP growth
helps predict recessions, as do movements in real house
price inflation, and declines in banks’ liquidity ratios.
• These factors appear to explain the observed correlation
between banking crises and recessions.
3. Indicator models of banking crises and recessions
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Model performance - recessions
Chart 2: Year-ahead predictions of recession
• Accurately predicts
whether an economy will
be in recession or not over
80% of the time
• But is prone to overpredict recessions, with
75% of predictions of
recessions turning out to
be inaccurate.
• But for some countries it
would have provided a
clear indication of
recession in 2008.
4. Predictive power of models
in the United Kingdom
Probability (Per cent)
0.9
(a)
Bank of England
Model
0.6
0.3
0.0
1985
1990
1995
2000
2005
2010
(a) Probability of zero four-quarter growth as implied by MPC GDP growth
fanchart. Probabilities below 5% are not published and are assumed to be
2.5% in the chart above.
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Model performance – banking crises
Chart 3: Year-ahead predictions of banking crises in
• Equations for banking
crises had a lower
probability of being
correct overall (at around
50-70%),
• The probability of a
predicting a crisis when
none occurred was over
90%.
• But still useful tool to
policymakers as flags
changes in the risk of a
banking crisis.
4. Predictive power of models
the United Kingdom
Probability (Per cent)
0.3
Model
0.2
0.1
0.0
1985
1990
1995
2000
2005
2010
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Conclusions
• Evidence for interdependency between recessions and
banking crises –reflecting common underlying factors.
• Banking sector capital and liquidity ratios and the current
account deficit are useful predictors of banking crises.
• Sharp falls in OECD leading indicators of GDP growth
helps predict recessions, as do movements in real house
price inflation, and declines in banks’ liquidity ratios.
• Our models tend to over-predict recessions and banking
crises.
• But they still provide policymakers with useful
information on changing risks of crises and recessions.
5. Conclusions
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